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Felten et al. (2021)

Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

AI-focusedPublicWorker-side
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Specific Type
AI exposure measure
Dataset Type
Cross-sectional
Institution
Princeton University; NYU Stern; Wharton
Institution Type
Academia
Level of Focus
Occupation
Most Granular Level
6-digit SOC occupation level
Perspective
Worker-side
Time Coverage
2018-2020
Frequency
One-time static snapshot
Sample Size
873 occupations; mTurk survey responses
Geographic Detail
National; state; county; industry
Occupational Classification
6-digit SOC 2018
Industrial Classification
4-digit NAICS
Other Classification
County-level (FIPS); ability-level
Key Variables
AIOE scores; 10 AI applications; 52 human abilities; crowdsourced relatedness ratings; AIIE industry scores; AIGE county scores
AI/Tech Tracking
10 AI applications (image recognition; speech recognition; text analysis; etc.)
Access Details
Publicly available (GitHub)
Notes
Modified from earlier 2018 methodology; includes extensions for newer AI applications; validated against patent data

Key Papers

Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses

Felten, Raj, Seamans (2021)

AI and the Future of Work in an Aging Economy

Pizzinelli, Tavares (2025)

Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms

Demirci, Hannane, Zhu (2024)

Mapping the Future of Occupations: Transformative and Destructive Effects of New Digital Technologies on Jobs

Fossen, Sorgner (2019)

Labour Market Exposure to AI: Cross-Country Differences and Distributional Implications

Pizzinelli (2023)

AI and Freelancers: Has the Inflection Point Arrived?

Qiao, Rui, Qian (2025)

Felten et al. (2021); Pizzinelli & Tavares (2025); Demirci et al. (2024); Fossen & Sorgner (2019); Pizzinelli (2023); Qiao et al. (2025)